1. Technical Field
The present invention relates to detecting lesions in medical images, and more particularly, to using efficient features for shape analysis of lesions in breast MR.
2. Discussion of the Related Art
A key process of detecting regions in breast MR, also referred to as magnetic resonance breast imaging, involves shape analysis and pharmacokinetic analysis of candidate regions. Most of the existing shape features are scalars that reflect, to some extent, the complexity of the lesion boundary. A popular shape feature is the square root of the surface area S1/2 divided by the cubic root of the volume V1/3 of a candidate region. See [Chen, W. and Giger, M. L. and Bick, U. A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images. Acad. Radiol., 13(1):63-72, 2006], for example. This feature shows how the shape deviates from a sphere, because a sphere attains the minimum of S1/2/V1/3.
Another well-known shape feature is fractal dimension, which is a scalar that is used, for example, in ONCAD by Penn Diagnostics and is described in Penn at al. [Penn, A. I., Loew, M. H. Estimating Fractal Dimension with Fractal Interpolation Function Models. IEEE Trans. Med. Imaging, 16(6):930-937, 1997], for example. The fractal dimension of an ordinary shape coincides with the ordinary definition of integer dimensions such as three dimensions for medical volumes. For a fractal shape that is defined recursively or by infinite iterations, its fractal dimension is higher than ordinary shapes and is a fractional number such as 3.55. Penn et al. used the fractional number to represent the complexity of the carcinoma shape. However, a carcinoma cannot have a dimension other than integers in a strict mathematical sense because it is not defined recursively nor by infinite iterations. Such scalars have limited descriptive power because they are only a one-dimensional projection of a very complicated feature.